Basic Statistics and Data Analysis

Lecture notes, MCQS of Statistics

p-value Interpretation and misinterpretation of p-value

p-value Interpretation

The P-value is a probability, with a value ranging from zero to one. It is measure of how much evidence we have against the null hypothesis. P-value is a way to express the likelihood that $H_0$ is not true. The smaller the p-value, the more evidence we have against $H_0$.

p-value can be defined as

The largest significance level at which we would accept the null hypothesis. It enables us to test hypothesis without first specifying a value for $\alpha$. OR

The probability of observing a sample value as extreme as, or more extreme than, the value observed, given that the null hypothesis is true.

If the P-value is smaller then the chosen significance level then $H_0$ (null hypothesis) is rejected even when it is true. If it is larger than the significance level $H_0$ is not rejected.

If the P-value is less than

  • 0.10, we have some evidence that $H_0$ is not true
  • 0.05, strong evidence that $H_0$ is not true
  • 0.01, Very strong evidence that $H_0$ is not true
  • 0.001, extremely strong evidence that $H_0$ is not true

Misinterpretation of a P-value

Many people misunderstand P-values. For example, if the P-value is 0.03 then it means that there is a 3% chance of observing a difference as large as you observed even if the two population means are same (i.e. the null hypothesis is true). It is tempting to conclude, therefore, that there is a 97% chance that the difference you observed reflects a real difference between populations and a 3% chance that the difference is due to chance. However, this would be an incorrect conclusion. What you can say is that random sampling from identical populations would lead to a difference smaller than you observed in 97% of experiments and larger than you observed in 3% of experiments.

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Muhammad Imdadullah

Student and Instructor of Statistics and business mathematics. Currently Ph.D. Scholar (Statistics), Bahauddin Zakariya University Multan. Like Applied Statistics and Mathematics and Statistical Computing. Statistical and Mathematical software used are: SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel. Like to use type-setting LaTeX for composing Articles, thesis etc.

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